|Table of Contents|

Vehicle and Pedestrian Detection Model Based on Lightweight SSD(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年01期
Page:
73-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Vehicle and Pedestrian Detection Model Based on Lightweight SSD
Author(s):
Zheng Dong1; Li Xiangqun1; Xu Xinzheng1; 2
(1.School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China)(2.Key Laboratory of Data Science and Intelligence Application,Fujian Province University,Zhangzhou 363000,China)
Keywords:
object detection; convolutional neural network; lightweight neural network; SSD; mobileNetv2
PACS:
TP193
DOI:
10.3969/j.issn.1001-4616.2019.01.012
Abstract:
In recent years,the object detection algorithm based on deep learning has developed rapidly. However,it can’t be widely used in embedded platforms because the network is too large. This paper optimized the model size of SSD(Single Shot Multi-box Detector)network,introduced the lightweight convolutional neural network—MobileNetv2,analyzed the inverted residual and linear bottleneck structure in MobileNetv2,and compared SSD and its lightweight version—SSDLite. We proposed a lightweight vehicle and pedestrian detection model which named LVP-DN(Lightweight Vehicle and Pedestrian Detection Network). First,the MobilNetv2 was used to instead of VGG as the basic network to perform feature extraction. Then,the SSDLite was used to replace the original structure,in order to reduce the model size and speed up the detection process. It is improved that the accuracy of network detection for pedestrians by optimizing the ratio of the default box. We compared the impact of three factors on network performance on the KITTI and PASCAL VOC datasets. The factors are the input image size,different basic network and whether used the pre-training models. The experimental results show that compared with other popular object detection models,the vehicle and pedestrian detection models proposed in this paper have achieved good results in the evaluation standards such as accuracy,speed,and model size.

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Last Update: 2019-03-30